{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Demo 02 互联网金融项目\n",
    "> 金融的核心在风控。 逾期客户用户画像的分析。 数据来源 拍拍贷平台 [下载地址](https://www.kesci.com/home/dataset/593ccb4523168e6e8923ab7f)\n",
    "\n",
    "### 分析思路\n",
    "\n",
    "  * 借款数据  LCIS.csv\n",
    "    1. 熟悉数据\n",
    "      1. 导入数据\n",
    "      2. 熟悉数据字段\n",
    "      3. 分类变量\n",
    "\n",
    "    2. 数据清洗\n",
    "      1. 统一变量名\n",
    "      2. 缺失值处理\n",
    "      3. 重复值、异常值处理\n",
    "\n",
    "    3. 逾期用户画像\n",
    "      1. 用户基本信息分析\n",
    "      2. 用户行为信息分析\n",
    "\n",
    "    4. 不良率分析\n",
    "      1. 随时间变化的趋势\n",
    "      2. 与年龄、性别的相关性\n",
    "      3.  与初始评级的相关性\n",
    "      4.与借款信息的相关性\n",
    "\n",
    "    5. 总结与建议"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "sns.set_style('darkgrid')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 292539 entries, 0 to 292538\n",
      "Data columns (total 37 columns):\n",
      " #   Column      Non-Null Count   Dtype  \n",
      "---  ------      --------------   -----  \n",
      " 0   ListingId   292539 non-null  object \n",
      " 1   借款金额        292539 non-null  int64  \n",
      " 2   借款期限        292539 non-null  int64  \n",
      " 3   借款利率        292539 non-null  float64\n",
      " 4   借款成功日期      292539 non-null  object \n",
      " 5   初始评级        292539 non-null  object \n",
      " 6   借款类型        292539 non-null  object \n",
      " 7   是否首标        292539 non-null  object \n",
      " 8   年龄          292539 non-null  int64  \n",
      " 9   性别          292539 non-null  object \n",
      " 10  手机认证        292539 non-null  object \n",
      " 11  户口认证        292539 non-null  object \n",
      " 12  视频认证        292539 non-null  object \n",
      " 13  学历认证        292539 non-null  object \n",
      " 14  征信认证        292539 non-null  object \n",
      " 15  淘宝认证        292539 non-null  object \n",
      " 16  历史成功借款次数    291336 non-null  float64\n",
      " 17  历史成功借款金额    291336 non-null  float64\n",
      " 18  总待还本金       292539 non-null  float64\n",
      " 19  历史正常还款期数    292539 non-null  int64  \n",
      " 20  历史逾期还款期数    292539 non-null  int64  \n",
      " 21  我的投资金额      292539 non-null  int64  \n",
      " 22  当前到期期数      292539 non-null  int64  \n",
      " 23  当前还款期数      292539 non-null  int64  \n",
      " 24  已还本金        292539 non-null  float64\n",
      " 25  已还利息        292539 non-null  float64\n",
      " 26  待还本金        292539 non-null  float64\n",
      " 27  待还利息        292539 non-null  float64\n",
      " 28  标当前逾期天数     292539 non-null  int64  \n",
      " 29  标当前状态       292539 non-null  object \n",
      " 30  上次还款日期      271490 non-null  object \n",
      " 31  上次还款本金      270290 non-null  float64\n",
      " 32  上次还款利息      270290 non-null  float64\n",
      " 33  下次计划还款日期    182563 non-null  object \n",
      " 34  下次计划还款本金    182563 non-null  float64\n",
      " 35  下次计划还款利息    181494 non-null  float64\n",
      " 36  recorddate  292130 non-null  object \n",
      "dtypes: float64(12), int64(9), object(16)\n",
      "memory usage: 82.6+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": "                借款金额           借款期限           借款利率             年龄  \\\ncount  292539.000000  292539.000000  292539.000000  292539.000000   \nmean     8516.123713      10.191974      17.783796      29.353949   \nstd     27584.913864       3.148704       3.375216       6.165157   \nmin       100.000000       1.000000       7.000000      18.000000   \n25%      3000.000000       6.000000      16.000000      25.000000   \n50%      4107.000000      12.000000      18.000000      28.000000   \n75%      7000.000000      12.000000      20.000000      32.000000   \nmax    500000.000000      24.000000      24.000000      65.000000   \n\n            历史成功借款次数      历史成功借款金额         总待还本金      历史正常还款期数       历史逾期还款期数  \\\ncount  291336.000000  2.913360e+05  2.925390e+05  2.925390e+05  292539.000000   \nmean        2.583769  1.513134e+04  4.499622e+03  7.664124e+01      18.366290   \nstd         5.081881  7.026413e+04  1.698939e+04  4.672302e+03    1256.978087   \nmin         0.000000  0.000000e+00  0.000000e+00  0.000000e+00       0.000000   \n25%         1.000000  1.000000e+03  0.000000e+00  0.000000e+00       0.000000   \n50%         2.000000  6.500000e+03  2.883070e+03  7.000000e+00       0.000000   \n75%         4.000000  1.369200e+04  5.890600e+03  1.500000e+01       0.000000   \nmax       487.000000  3.856476e+06  1.697706e+06  1.625000e+06  524034.000000   \n\n              我的投资金额  ...         当前还款期数           已还本金           已还利息  \\\ncount  292539.000000  ...  292539.000000  292539.000000  292539.000000   \nmean       96.347625  ...       5.653215      72.086067       4.796278   \nstd       117.748925  ...      11.146470     122.297594       6.482616   \nmin         0.000000  ...       0.000000       0.000000       0.000000   \n25%        50.000000  ...       2.000000      12.000000       1.560000   \n50%        50.000000  ...       5.000000      28.260000       2.660000   \n75%        58.000000  ...       7.000000      50.000000       4.890000   \nmax      2190.000000  ...    1100.000000    2190.000000     269.600000   \n\n                待还本金           待还利息        标当前逾期天数         上次还款本金  \\\ncount  292539.000000  292539.000000  292539.000000  270290.000000   \nmean       24.475503       1.669001       3.185104      21.278687   \nstd        30.993081       2.305682      27.908310      48.777880   \nmin         0.000000       0.000000       0.000000       0.000000   \n25%         0.000000       0.000000       0.000000       4.220000   \n50%        21.540000       0.750000       0.000000       6.730000   \n75%        41.980000       2.910000       0.000000      16.740000   \nmax      1100.000000      87.740000     681.000000    1413.000000   \n\n              上次还款利息       下次计划还款本金       下次计划还款利息  \ncount  270290.000000  182563.000000  181494.000000  \nmean        0.481281       6.042533       0.584434  \nstd         0.421215       4.374562       0.331167  \nmin         0.000000       0.000000       0.000000  \n25%         0.150000       4.130000       0.370000  \n50%         0.440000       4.430000       0.570000  \n75%         0.700000       8.120000       0.760000  \nmax        11.770000      96.900000       8.920000  \n\n[8 rows x 21 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>借款金额</th>\n      <th>借款期限</th>\n      <th>借款利率</th>\n      <th>年龄</th>\n      <th>历史成功借款次数</th>\n      <th>历史成功借款金额</th>\n      <th>总待还本金</th>\n      <th>历史正常还款期数</th>\n      <th>历史逾期还款期数</th>\n      <th>我的投资金额</th>\n      <th>...</th>\n      <th>当前还款期数</th>\n      <th>已还本金</th>\n      <th>已还利息</th>\n      <th>待还本金</th>\n      <th>待还利息</th>\n      <th>标当前逾期天数</th>\n      <th>上次还款本金</th>\n      <th>上次还款利息</th>\n      <th>下次计划还款本金</th>\n      <th>下次计划还款利息</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>292539.000000</td>\n      <td>292539.000000</td>\n      <td>292539.000000</td>\n      <td>292539.000000</td>\n      <td>291336.000000</td>\n      <td>2.913360e+05</td>\n      <td>2.925390e+05</td>\n      <td>2.925390e+05</td>\n      <td>292539.000000</td>\n      <td>292539.000000</td>\n      <td>...</td>\n      <td>292539.000000</td>\n      <td>292539.000000</td>\n      <td>292539.000000</td>\n      <td>292539.000000</td>\n      <td>292539.000000</td>\n      <td>292539.000000</td>\n      <td>270290.000000</td>\n      <td>270290.000000</td>\n      <td>182563.000000</td>\n      <td>181494.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>8516.123713</td>\n      <td>10.191974</td>\n      <td>17.783796</td>\n      <td>29.353949</td>\n      <td>2.583769</td>\n      <td>1.513134e+04</td>\n      <td>4.499622e+03</td>\n      <td>7.664124e+01</td>\n      <td>18.366290</td>\n      <td>96.347625</td>\n      <td>...</td>\n      <td>5.653215</td>\n      <td>72.086067</td>\n      <td>4.796278</td>\n      <td>24.475503</td>\n      <td>1.669001</td>\n      <td>3.185104</td>\n      <td>21.278687</td>\n      <td>0.481281</td>\n      <td>6.042533</td>\n      <td>0.584434</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>27584.913864</td>\n      <td>3.148704</td>\n      <td>3.375216</td>\n      <td>6.165157</td>\n      <td>5.081881</td>\n      <td>7.026413e+04</td>\n      <td>1.698939e+04</td>\n      <td>4.672302e+03</td>\n      <td>1256.978087</td>\n      <td>117.748925</td>\n      <td>...</td>\n      <td>11.146470</td>\n      <td>122.297594</td>\n      <td>6.482616</td>\n      <td>30.993081</td>\n      <td>2.305682</td>\n      <td>27.908310</td>\n      <td>48.777880</td>\n      <td>0.421215</td>\n      <td>4.374562</td>\n      <td>0.331167</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>100.000000</td>\n      <td>1.000000</td>\n      <td>7.000000</td>\n      <td>18.000000</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>3000.000000</td>\n      <td>6.000000</td>\n      <td>16.000000</td>\n      <td>25.000000</td>\n      <td>1.000000</td>\n      <td>1.000000e+03</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>50.000000</td>\n      <td>...</td>\n      <td>2.000000</td>\n      <td>12.000000</td>\n      <td>1.560000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>4.220000</td>\n      <td>0.150000</td>\n      <td>4.130000</td>\n      <td>0.370000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>4107.000000</td>\n      <td>12.000000</td>\n      <td>18.000000</td>\n      <td>28.000000</td>\n      <td>2.000000</td>\n      <td>6.500000e+03</td>\n      <td>2.883070e+03</td>\n      <td>7.000000e+00</td>\n      <td>0.000000</td>\n      <td>50.000000</td>\n      <td>...</td>\n      <td>5.000000</td>\n      <td>28.260000</td>\n      <td>2.660000</td>\n      <td>21.540000</td>\n      <td>0.750000</td>\n      <td>0.000000</td>\n      <td>6.730000</td>\n      <td>0.440000</td>\n      <td>4.430000</td>\n      <td>0.570000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>7000.000000</td>\n      <td>12.000000</td>\n      <td>20.000000</td>\n      <td>32.000000</td>\n      <td>4.000000</td>\n      <td>1.369200e+04</td>\n      <td>5.890600e+03</td>\n      <td>1.500000e+01</td>\n      <td>0.000000</td>\n      <td>58.000000</td>\n      <td>...</td>\n      <td>7.000000</td>\n      <td>50.000000</td>\n      <td>4.890000</td>\n      <td>41.980000</td>\n      <td>2.910000</td>\n      <td>0.000000</td>\n      <td>16.740000</td>\n      <td>0.700000</td>\n      <td>8.120000</td>\n      <td>0.760000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>500000.000000</td>\n      <td>24.000000</td>\n      <td>24.000000</td>\n      <td>65.000000</td>\n      <td>487.000000</td>\n      <td>3.856476e+06</td>\n      <td>1.697706e+06</td>\n      <td>1.625000e+06</td>\n      <td>524034.000000</td>\n      <td>2190.000000</td>\n      <td>...</td>\n      <td>1100.000000</td>\n      <td>2190.000000</td>\n      <td>269.600000</td>\n      <td>1100.000000</td>\n      <td>87.740000</td>\n      <td>681.000000</td>\n      <td>1413.000000</td>\n      <td>11.770000</td>\n      <td>96.900000</td>\n      <td>8.920000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 21 columns</p>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/LCIS.csv', dtype={'ListingId': str})\n",
    "data.info() # 查看数据 30w 行 * 37列\n",
    "data.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 数据预处理"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "cols = {'ListingId': '列表序号', 'recorddate':'记录日期'}\n",
    "data.rename(columns=cols, inplace=True)\n",
    "miss_rate = pd.DataFrame(data.apply(lambda x : sum(x.isnull())/len(x)))\n",
    "miss_rate.columns = ['缺失率']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "历史成功借款次数     0.411%\n历史成功借款金额     0.411%\n上次还款日期       7.195%\n上次还款本金       7.605%\n上次还款利息       7.605%\n下次计划还款日期    37.594%\n下次计划还款本金    37.594%\n下次计划还款利息    37.959%\n记录日期         0.140%\nName: 缺失率, dtype: object"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miss_rate[miss_rate['缺失率'] > 0]['缺失率'].apply(lambda  x: format(x,'.3%')) # 找到有缺失的数据"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "已还清      109168\n正常还款中       674\n5            26\n2.65         16\n6.47          9\n6.77          8\n2.35          8\n4.43          7\n6.27          7\n3.42          6\nName: 标当前状态, dtype: int64"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['下次计划还款日期'].isnull()].head() # 查看空字段\n",
    "data[data['下次计划还款日期'].isnull()]['标当前状态'].value_counts()[:10] # 估计空字段的原因"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "            列表序号   借款金额  借款期限  借款利率     借款成功日期 初始评级   借款类型 是否首标  年龄 性别  ...  \\\n291539  20871939  11000    12  18.0  2016/9/27    B  APP闪电    否  29  女  ...   \n291540  20988739   6054    12  18.0  2016/9/28    B  APP闪电    否  27  女  ...   \n291541  21043539   3176     6  18.0  2016/9/29    B     其他    否  23  女  ...   \n291614  13713501  10000     6  18.0  2016/6/12    B  APP闪电    否  30  男  ...   \n292140  16734296   2216    12  22.0  2016/7/30    C     其他    否  36  男  ...   \n\n        待还利息 标当前逾期天数  标当前状态 上次还款日期 上次还款本金 上次还款利息    下次计划还款日期  下次计划还款本金  \\\n291539  5.00       0  正常还款中    NaN    NaN    NaN  2016/10/27      3.83   \n291540  5.00       0  正常还款中    NaN    NaN    NaN  2016/10/28      3.83   \n291541  2.65       0  正常还款中    NaN    NaN    NaN  2016/10/29      8.02   \n291614  2.65     232    逾期中    NaN    NaN    NaN   2016/7/12      8.02   \n292140  6.77     183    逾期中    NaN    NaN    NaN   2016/8/30      4.13   \n\n        下次计划还款利息       记录日期  \n291539      0.75  2016/9/30  \n291540      0.75  2016/9/30  \n291541      0.75  2016/9/30  \n291614      0.75  2017/2/28  \n292140      1.01  2017/2/28  \n\n[5 rows x 37 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>列表序号</th>\n      <th>借款金额</th>\n      <th>借款期限</th>\n      <th>借款利率</th>\n      <th>借款成功日期</th>\n      <th>初始评级</th>\n      <th>借款类型</th>\n      <th>是否首标</th>\n      <th>年龄</th>\n      <th>性别</th>\n      <th>...</th>\n      <th>待还利息</th>\n      <th>标当前逾期天数</th>\n      <th>标当前状态</th>\n      <th>上次还款日期</th>\n      <th>上次还款本金</th>\n      <th>上次还款利息</th>\n      <th>下次计划还款日期</th>\n      <th>下次计划还款本金</th>\n      <th>下次计划还款利息</th>\n      <th>记录日期</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>291539</th>\n      <td>20871939</td>\n      <td>11000</td>\n      <td>12</td>\n      <td>18.0</td>\n      <td>2016/9/27</td>\n      <td>B</td>\n      <td>APP闪电</td>\n      <td>否</td>\n      <td>29</td>\n      <td>女</td>\n      <td>...</td>\n      <td>5.00</td>\n      <td>0</td>\n      <td>正常还款中</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2016/10/27</td>\n      <td>3.83</td>\n      <td>0.75</td>\n      <td>2016/9/30</td>\n    </tr>\n    <tr>\n      <th>291540</th>\n      <td>20988739</td>\n      <td>6054</td>\n      <td>12</td>\n      <td>18.0</td>\n      <td>2016/9/28</td>\n      <td>B</td>\n      <td>APP闪电</td>\n      <td>否</td>\n      <td>27</td>\n      <td>女</td>\n      <td>...</td>\n      <td>5.00</td>\n      <td>0</td>\n      <td>正常还款中</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2016/10/28</td>\n      <td>3.83</td>\n      <td>0.75</td>\n      <td>2016/9/30</td>\n    </tr>\n    <tr>\n      <th>291541</th>\n      <td>21043539</td>\n      <td>3176</td>\n      <td>6</td>\n      <td>18.0</td>\n      <td>2016/9/29</td>\n      <td>B</td>\n      <td>其他</td>\n      <td>否</td>\n      <td>23</td>\n      <td>女</td>\n      <td>...</td>\n      <td>2.65</td>\n      <td>0</td>\n      <td>正常还款中</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2016/10/29</td>\n      <td>8.02</td>\n      <td>0.75</td>\n      <td>2016/9/30</td>\n    </tr>\n    <tr>\n      <th>291614</th>\n      <td>13713501</td>\n      <td>10000</td>\n      <td>6</td>\n      <td>18.0</td>\n      <td>2016/6/12</td>\n      <td>B</td>\n      <td>APP闪电</td>\n      <td>否</td>\n      <td>30</td>\n      <td>男</td>\n      <td>...</td>\n      <td>2.65</td>\n      <td>232</td>\n      <td>逾期中</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2016/7/12</td>\n      <td>8.02</td>\n      <td>0.75</td>\n      <td>2017/2/28</td>\n    </tr>\n    <tr>\n      <th>292140</th>\n      <td>16734296</td>\n      <td>2216</td>\n      <td>12</td>\n      <td>22.0</td>\n      <td>2016/7/30</td>\n      <td>C</td>\n      <td>其他</td>\n      <td>否</td>\n      <td>36</td>\n      <td>男</td>\n      <td>...</td>\n      <td>6.77</td>\n      <td>183</td>\n      <td>逾期中</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2016/8/30</td>\n      <td>4.13</td>\n      <td>1.01</td>\n      <td>2017/2/28</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 37 columns</p>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['上次还款利息'].isnull()].tail() # 查看空字段\n",
    "# data[data['上次还款利息'].isnull()]['标当前状态'].value_counts()[:10] #\n",
    "# 估计空字段的原因"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "           列表序号   借款金额  借款期限  借款利率     借款成功日期 初始评级 借款类型 是否首标  年龄 性别  ...  \\\n118020  1625423   4140     3  12.0   2015/1/8   AA   普通    否  36  男  ...   \n118021  1678223   4921    12  12.0  2015/1/23   AA   普通    否  27  女  ...   \n118022  1710623   5728    12  12.0   2015/2/1   AA   普通    否  28  女  ...   \n118024  1993823   3869    12  12.0  2015/3/23   AA   普通    否  29  男  ...   \n118025  1994223  14053    12  18.0  2015/3/17    D   其他    否  51  男  ...   \n\n         待还利息 标当前逾期天数 标当前状态 上次还款日期 上次还款本金 上次还款利息  下次计划还款日期  下次计划还款本金  \\\n118020   0.84       0     0      0    NaN    NaN     16.84      0.00   \n118021   9.68       0     0      0    NaN    NaN     13.07      0.07   \n118022  33.04       0     0      0    NaN    NaN     44.06      0.36   \n118024  20.99       0     0      0    NaN    NaN    298.93      0.00   \n118025   9.92       0     0      0    NaN    NaN       9.1      0.06   \n\n        下次计划还款利息  记录日期  \n118020       NaN   NaN  \n118021       NaN   NaN  \n118022       NaN   NaN  \n118024       NaN   NaN  \n118025       NaN   NaN  \n\n[5 rows x 37 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>列表序号</th>\n      <th>借款金额</th>\n      <th>借款期限</th>\n      <th>借款利率</th>\n      <th>借款成功日期</th>\n      <th>初始评级</th>\n      <th>借款类型</th>\n      <th>是否首标</th>\n      <th>年龄</th>\n      <th>性别</th>\n      <th>...</th>\n      <th>待还利息</th>\n      <th>标当前逾期天数</th>\n      <th>标当前状态</th>\n      <th>上次还款日期</th>\n      <th>上次还款本金</th>\n      <th>上次还款利息</th>\n      <th>下次计划还款日期</th>\n      <th>下次计划还款本金</th>\n      <th>下次计划还款利息</th>\n      <th>记录日期</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>118020</th>\n      <td>1625423</td>\n      <td>4140</td>\n      <td>3</td>\n      <td>12.0</td>\n      <td>2015/1/8</td>\n      <td>AA</td>\n      <td>普通</td>\n      <td>否</td>\n      <td>36</td>\n      <td>男</td>\n      <td>...</td>\n      <td>0.84</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>16.84</td>\n      <td>0.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>118021</th>\n      <td>1678223</td>\n      <td>4921</td>\n      <td>12</td>\n      <td>12.0</td>\n      <td>2015/1/23</td>\n      <td>AA</td>\n      <td>普通</td>\n      <td>否</td>\n      <td>27</td>\n      <td>女</td>\n      <td>...</td>\n      <td>9.68</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>13.07</td>\n      <td>0.07</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>118022</th>\n      <td>1710623</td>\n      <td>5728</td>\n      <td>12</td>\n      <td>12.0</td>\n      <td>2015/2/1</td>\n      <td>AA</td>\n      <td>普通</td>\n      <td>否</td>\n      <td>28</td>\n      <td>女</td>\n      <td>...</td>\n      <td>33.04</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>44.06</td>\n      <td>0.36</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>118024</th>\n      <td>1993823</td>\n      <td>3869</td>\n      <td>12</td>\n      <td>12.0</td>\n      <td>2015/3/23</td>\n      <td>AA</td>\n      <td>普通</td>\n      <td>否</td>\n      <td>29</td>\n      <td>男</td>\n      <td>...</td>\n      <td>20.99</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>298.93</td>\n      <td>0.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>118025</th>\n      <td>1994223</td>\n      <td>14053</td>\n      <td>12</td>\n      <td>18.0</td>\n      <td>2015/3/17</td>\n      <td>D</td>\n      <td>其他</td>\n      <td>否</td>\n      <td>51</td>\n      <td>男</td>\n      <td>...</td>\n      <td>9.92</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9.1</td>\n      <td>0.06</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 37 columns</p>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['记录日期'].isnull()].head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "data.dropna(subset=['记录日期'], how='any', inplace=True ) # 删除记录日期为空的数据"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "data[data.duplicated()] # 查看重复的数据"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "data.drop_duplicates(inplace=True)  # 重复数据的清洗"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['手机认证'].value_counts().plot(kind='bar') # 发现有异常值"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "data = data[(data['手机认证'] =='成功认证' ) | (data['手机认证'] =='未成功认证' )]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['户口认证'].value_counts().plot(kind='bar') # 寻找其他异常值\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 构建模型\n",
    "1. 不同性别的放贷比例与逾期关系"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "标当前状态    已还清   正常还款中   逾期中\n性别                        \n女      29881   68967  2857\n男      79248  103538  6739",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>标当前状态</th>\n      <th>已还清</th>\n      <th>正常还款中</th>\n      <th>逾期中</th>\n    </tr>\n    <tr>\n      <th>性别</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>女</th>\n      <td>29881</td>\n      <td>68967</td>\n      <td>2857</td>\n    </tr>\n    <tr>\n      <th>男</th>\n      <td>79248</td>\n      <td>103538</td>\n      <td>6739</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gender = pd.pivot_table(data=data, columns='标当前状态', index='性别', values='列表序号', aggfunc=np.size)\n",
    "df_gender # 查看 性别 和 状态的数量"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "标当前状态    已还清   正常还款中   逾期中    借款笔数占比    逾期笔数占比\n性别                                            \n女      29881   68967  2857  0.349226  0.028091\n男      79248  103538  6739  0.650774  0.035557",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>标当前状态</th>\n      <th>已还清</th>\n      <th>正常还款中</th>\n      <th>逾期中</th>\n      <th>借款笔数占比</th>\n      <th>逾期笔数占比</th>\n    </tr>\n    <tr>\n      <th>性别</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>女</th>\n      <td>29881</td>\n      <td>68967</td>\n      <td>2857</td>\n      <td>0.349226</td>\n      <td>0.028091</td>\n    </tr>\n    <tr>\n      <th>男</th>\n      <td>79248</td>\n      <td>103538</td>\n      <td>6739</td>\n      <td>0.650774</td>\n      <td>0.035557</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gender['借款笔数占比'] = df_gender.apply(np.sum, axis=1) / df_gender.sum().sum() # 男女借款的比例\n",
    "\n",
    "df_gender['逾期笔数占比'] = df_gender['逾期中'] / df_gender.sum(axis=1)\n",
    "df_gender"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 0.98, '性别画像')"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1152x648 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "plt.subplot(121)\n",
    "plt.bar(x = df_gender.index, height=df_gender['借款笔数占比'] , color=['c','g'])\n",
    "plt.title('男女借款的比例')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.bar(x = df_gender.index, height=df_gender['逾期笔数占比'] , color=['c','g'])\n",
    "plt.title('男女逾期情况')\n",
    "plt.suptitle('性别画像')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "df_age = data.groupby(['年龄'])['借款金额'].sum()\n",
    "df_age = pd.DataFrame(df_age)\n",
    "df_age['借款金额累计'] = df_age['借款金额'].cumsum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "df_age['借款金额占比'] = df_age['借款金额累计'] /  df_age['借款金额'].sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8101917138984793"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_num = df_age[df_age['借款金额占比'] > 0.8].index[0] # 取第一个\n",
    "cum_percent = df_age.loc[index_num, '借款金额占比']\n",
    "cum_percent"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1152x648 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "plt.bar(x=df_age.index, height=df_age['借款金额'], color='b',alpha=0.5)\n",
    "plt.axvline(x=index_num, color='orange', linestyle='--', alpha=0.6)\n",
    "df_age['借款金额占比'].plot(style='--ob', secondary_y=True)\n",
    "plt.text(index_num+0.4 , cum_percent, '累计占比：%0.3f%%' % (cum_percent * 100), color='green')\n",
    "plt.xlabel('年龄', fontdict={'size': 40})\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "# 考虑年龄和逾期\n",
    "age_bins = [17, 24, 30 , 36, 42, 48, 54, 65]\n",
    "data['age_bin'] = pd.cut(data['年龄'], age_bins, right=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [],
   "source": [
    "df_age = pd.pivot_table(data=data, columns='标当前状态', index='age_bin', values='列表序号', aggfunc=np.size)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [],
   "source": [
    "df_age['借款笔数'] = df_age.sum(axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [
    {
     "data": {
      "text/plain": "标当前状态       已还清  正常还款中   逾期中    借款笔数    借款笔数分布      逾期占比\nage_bin                                                 \n(17, 24]  21687  38187  2092   61966  0.212773  0.033760\n(24, 30]  47594  79216  4023  130833  0.449243  0.030749\n(30, 36]  24645  35916  2068   62629  0.215050  0.033020\n(36, 42]   9267  11950   881   22098  0.075878  0.039868\n(42, 48]   4631   5673   427   10731  0.036847  0.039791\n(48, 54]   1225   1467    94    2786  0.009566  0.033740\n(54, 65]     80     96    11     187  0.000642  0.058824",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>标当前状态</th>\n      <th>已还清</th>\n      <th>正常还款中</th>\n      <th>逾期中</th>\n      <th>借款笔数</th>\n      <th>借款笔数分布</th>\n      <th>逾期占比</th>\n    </tr>\n    <tr>\n      <th>age_bin</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>(17, 24]</th>\n      <td>21687</td>\n      <td>38187</td>\n      <td>2092</td>\n      <td>61966</td>\n      <td>0.212773</td>\n      <td>0.033760</td>\n    </tr>\n    <tr>\n      <th>(24, 30]</th>\n      <td>47594</td>\n      <td>79216</td>\n      <td>4023</td>\n      <td>130833</td>\n      <td>0.449243</td>\n      <td>0.030749</td>\n    </tr>\n    <tr>\n      <th>(30, 36]</th>\n      <td>24645</td>\n      <td>35916</td>\n      <td>2068</td>\n      <td>62629</td>\n      <td>0.215050</td>\n      <td>0.033020</td>\n    </tr>\n    <tr>\n      <th>(36, 42]</th>\n      <td>9267</td>\n      <td>11950</td>\n      <td>881</td>\n      <td>22098</td>\n      <td>0.075878</td>\n      <td>0.039868</td>\n    </tr>\n    <tr>\n      <th>(42, 48]</th>\n      <td>4631</td>\n      <td>5673</td>\n      <td>427</td>\n      <td>10731</td>\n      <td>0.036847</td>\n      <td>0.039791</td>\n    </tr>\n    <tr>\n      <th>(48, 54]</th>\n      <td>1225</td>\n      <td>1467</td>\n      <td>94</td>\n      <td>2786</td>\n      <td>0.009566</td>\n      <td>0.033740</td>\n    </tr>\n    <tr>\n      <th>(54, 65]</th>\n      <td>80</td>\n      <td>96</td>\n      <td>11</td>\n      <td>187</td>\n      <td>0.000642</td>\n      <td>0.058824</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age['借款笔数分布'] = df_age['借款笔数'] /df_age['借款笔数'].sum()\n",
    "df_age['逾期占比'] = df_age['逾期中'] /df_age['借款笔数']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "data": {
      "text/plain": "标当前状态       已还清  正常还款中   逾期中    借款笔数    借款笔数分布      逾期占比  借款笔数分布%   逾期占比%\nage_bin                                                                  \n(17, 24]  21687  38187  2092   61966  0.212773  0.033760  21.277%  3.376%\n(24, 30]  47594  79216  4023  130833  0.449243  0.030749  44.924%  3.075%\n(30, 36]  24645  35916  2068   62629  0.215050  0.033020  21.505%  3.302%\n(36, 42]   9267  11950   881   22098  0.075878  0.039868   7.588%  3.987%\n(42, 48]   4631   5673   427   10731  0.036847  0.039791   3.685%  3.979%\n(48, 54]   1225   1467    94    2786  0.009566  0.033740   0.957%  3.374%\n(54, 65]     80     96    11     187  0.000642  0.058824   0.064%  5.882%",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>标当前状态</th>\n      <th>已还清</th>\n      <th>正常还款中</th>\n      <th>逾期中</th>\n      <th>借款笔数</th>\n      <th>借款笔数分布</th>\n      <th>逾期占比</th>\n      <th>借款笔数分布%</th>\n      <th>逾期占比%</th>\n    </tr>\n    <tr>\n      <th>age_bin</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>(17, 24]</th>\n      <td>21687</td>\n      <td>38187</td>\n      <td>2092</td>\n      <td>61966</td>\n      <td>0.212773</td>\n      <td>0.033760</td>\n      <td>21.277%</td>\n      <td>3.376%</td>\n    </tr>\n    <tr>\n      <th>(24, 30]</th>\n      <td>47594</td>\n      <td>79216</td>\n      <td>4023</td>\n      <td>130833</td>\n      <td>0.449243</td>\n      <td>0.030749</td>\n      <td>44.924%</td>\n      <td>3.075%</td>\n    </tr>\n    <tr>\n      <th>(30, 36]</th>\n      <td>24645</td>\n      <td>35916</td>\n      <td>2068</td>\n      <td>62629</td>\n      <td>0.215050</td>\n      <td>0.033020</td>\n      <td>21.505%</td>\n      <td>3.302%</td>\n    </tr>\n    <tr>\n      <th>(36, 42]</th>\n      <td>9267</td>\n      <td>11950</td>\n      <td>881</td>\n      <td>22098</td>\n      <td>0.075878</td>\n      <td>0.039868</td>\n      <td>7.588%</td>\n      <td>3.987%</td>\n    </tr>\n    <tr>\n      <th>(42, 48]</th>\n      <td>4631</td>\n      <td>5673</td>\n      <td>427</td>\n      <td>10731</td>\n      <td>0.036847</td>\n      <td>0.039791</td>\n      <td>3.685%</td>\n      <td>3.979%</td>\n    </tr>\n    <tr>\n      <th>(48, 54]</th>\n      <td>1225</td>\n      <td>1467</td>\n      <td>94</td>\n      <td>2786</td>\n      <td>0.009566</td>\n      <td>0.033740</td>\n      <td>0.957%</td>\n      <td>3.374%</td>\n    </tr>\n    <tr>\n      <th>(54, 65]</th>\n      <td>80</td>\n      <td>96</td>\n      <td>11</td>\n      <td>187</td>\n      <td>0.000642</td>\n      <td>0.058824</td>\n      <td>0.064%</td>\n      <td>5.882%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age['借款笔数分布%'] = df_age['借款笔数分布'].apply(lambda x: format(x, '.3%'))\n",
    "df_age['逾期占比%'] = df_age['逾期占比'].apply(lambda x: format(x, '.3%'))\n",
    "\n",
    "df_age\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1152x648 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "plt.figure(figsize=(16,9))\n",
    "\n",
    "df_age['借款笔数分布'].plot(kind='bar', rot=45, style='--ob')\n",
    "plt.xlabel('年龄分段')\n",
    "df_age['逾期占比'].plot( rot=45, style='- or', secondary_y=True)\n",
    "\n",
    "plt.show()\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [],
   "source": [
    "# 考虑学历和逾期\n",
    "df_edu = pd.pivot_table(data=data, columns='标当前状态', index='学历认证', values='列表序号', aggfunc=np.size)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "标当前状态    已还清   正常还款中   逾期中    借款笔数    借款笔数分布      逾期占比\n学历认证                                                  \n成功认证   41431  102676  4019  148126  0.508622  0.027132\n未成功认证  67698   69829  5577  143104  0.491378  0.038972",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>标当前状态</th>\n      <th>已还清</th>\n      <th>正常还款中</th>\n      <th>逾期中</th>\n      <th>借款笔数</th>\n      <th>借款笔数分布</th>\n      <th>逾期占比</th>\n    </tr>\n    <tr>\n      <th>学历认证</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>成功认证</th>\n      <td>41431</td>\n      <td>102676</td>\n      <td>4019</td>\n      <td>148126</td>\n      <td>0.508622</td>\n      <td>0.027132</td>\n    </tr>\n    <tr>\n      <th>未成功认证</th>\n      <td>67698</td>\n      <td>69829</td>\n      <td>5577</td>\n      <td>143104</td>\n      <td>0.491378</td>\n      <td>0.038972</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_edu['借款笔数'] = df_edu.sum(axis=1)\n",
    "df_edu['借款笔数分布'] = df_edu['借款笔数'] /df_edu['借款笔数'].sum()\n",
    "df_edu['逾期占比'] = df_edu['逾期中'] /df_edu['借款笔数']\n",
    "df_edu"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1152x648 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "plt.figure(figsize=(16,9))\n",
    "plt.subplot(121)\n",
    "plt.pie(x =df_edu['借款笔数分布'], labels=['认证成功','未成功认证'],colors=['red','yellow'],\n",
    "        autopct='%.1f%%', pctdistance=0.5)\n",
    "plt.subplot(122)\n",
    "plt.bar(x=df_edu.index, height=df_edu['逾期占比'])\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [],
   "source": [
    "# 定义函数\n",
    "def trans(data, col, index):\n",
    "  df = pd.pivot_table(data=data, columns=col, index=index, values='列表序号', aggfunc=np.size)\n",
    "  df['借款笔数'] = df.apply(np.sum, axis=1)\n",
    "  df['借款笔数占比'] = df['借款笔数'] /df['借款笔数'].sum()\n",
    "  df['逾期占比'] = df['逾期中'] /df['借款笔数']\n",
    "  # 画图\n",
    "  plt.figure(figsize=(16,9))\n",
    "  plt.subplot(121)\n",
    "  plt.pie(x =df['借款笔数占比'], labels=['认证成功','未成功认证'], colors=['orange','red'],\n",
    "          autopct='%.1f%%', pctdistance=0.5, labeldistance=1.1)\n",
    "  plt.title('%s占比'% index)\n",
    "  plt.subplot(122)\n",
    "  plt.bar(x=df_edu.index,color=['orange','red'], height=df_edu['逾期占比'])\n",
    "  plt.title('不同%s人逾期'% index)\n",
    "  plt.suptitle('%s客户画像' % index)\n",
    "  plt.show()\n",
    "  return df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1152x648 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "标当前状态     已还清   正常还款中   逾期中    借款笔数    借款笔数占比      逾期占比\n淘宝认证                                                   \n成功认证     1073     549    48    1670  0.005734  0.028743\n未成功认证  108056  171956  9548  289560  0.994266  0.032974",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>标当前状态</th>\n      <th>已还清</th>\n      <th>正常还款中</th>\n      <th>逾期中</th>\n      <th>借款笔数</th>\n      <th>借款笔数占比</th>\n      <th>逾期占比</th>\n    </tr>\n    <tr>\n      <th>淘宝认证</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>成功认证</th>\n      <td>1073</td>\n      <td>549</td>\n      <td>48</td>\n      <td>1670</td>\n      <td>0.005734</td>\n      <td>0.028743</td>\n    </tr>\n    <tr>\n      <th>未成功认证</th>\n      <td>108056</td>\n      <td>171956</td>\n      <td>9548</td>\n      <td>289560</td>\n      <td>0.994266</td>\n      <td>0.032974</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans(data, '标当前状态','淘宝认证')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "trans(data, '标当前状态','手机认证')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1152x648 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "标当前状态    已还清   正常还款中   逾期中    借款笔数    借款笔数占比      逾期占比\n视频认证                                                  \n成功认证   15490    8302  1041   24833  0.085269  0.041920\n未成功认证  93639  164203  8555  266397  0.914731  0.032114",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>标当前状态</th>\n      <th>已还清</th>\n      <th>正常还款中</th>\n      <th>逾期中</th>\n      <th>借款笔数</th>\n      <th>借款笔数占比</th>\n      <th>逾期占比</th>\n    </tr>\n    <tr>\n      <th>视频认证</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>成功认证</th>\n      <td>15490</td>\n      <td>8302</td>\n      <td>1041</td>\n      <td>24833</td>\n      <td>0.085269</td>\n      <td>0.041920</td>\n    </tr>\n    <tr>\n      <th>未成功认证</th>\n      <td>93639</td>\n      <td>164203</td>\n      <td>8555</td>\n      <td>266397</td>\n      <td>0.914731</td>\n      <td>0.032114</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans(data, '标当前状态','视频认证')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 总结\n",
    "\n",
    "* 男性的逾期率 是女性逾期率的2倍\n",
    "* 年龄，36岁以下的借款数占80%以上"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}